Zhang Jian, Xu Jihai, Yu Jiapei, Chen Hong, Hong Xin, Zhang Songou, Wang Xin, Shen Chengchun
Department of Orthopaedics, Ningbo No.6 Hospital, Ningbo, China.
Ningbo Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Ningbo, China.
Front Bioeng Biotechnol. 2025 Jul 31;13:1644261. doi: 10.3389/fbioe.2025.1644261. eCollection 2025.
This study aims to develop and validate an interpretable machine learning model for predicting avascular necrosis (AVN) following talar fracture, thereby aiding in personalized prevention and treatment.
A retrospective cohort study included patients undergoing surgical intervention for talar fractures at Ningbo No.6 Hospital between January 2018 and December 2023. Multidimensional data encompassing demographic characteristics, fracture-related variables, surgery-related parameters, and follow-up information were collected. Patients were randomly allocated to the training and testing sets in a 7:3 ratio. Potential risk factors for postoperative AVN were screened using univariate and multivariate logistic regression analyses. Six machine learning algorithms were employed to construct the prediction models. The performance of the prediction model was evaluated utilizing metrics including area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), precision, recall, and F1 score. The SHapley Additive exPlanations (SHAP) provided global and local explanations for the optimal model.
A total of 207 patients with talar fractures were enrolled in our study, with 45 (21.74%) developed AVN, and 162 (78.26%) did not. Univariate and multivariable logistic regression identified six independent risk factors including body mass index (BMI), fracture classification, concomitant ipsilateral foot and ankle fractures, smoking, quality of fracture reduction, and fracture type. Performance evaluation demonstrated that Extreme Gradient Boosting (XGBoost model) achieved high AUC values with superior specificity and sensitivity in both the training and testing sets. The SHAP was performed to analyze the relative importance of features within the model visually and illustrate the impact of each feature on individual patient outcomes.
This study successfully developed and validated an interpretable machine learning model incorporating key clinical and surgical variables to predict AVN following talar fractures. The prediction model identified high-risk patients and critical modifiable factors, facilitating personalized prevention strategies to mitigate this severe complication.
本研究旨在开发并验证一种可解释的机器学习模型，用于预测距骨骨折后的缺血性坏死（AVN），从而有助于个性化的预防和治疗。
一项回顾性队列研究纳入了2018年1月至2023年12月期间在宁波市第六医院接受距骨骨折手术干预的患者。收集了包括人口统计学特征、骨折相关变量、手术相关参数和随访信息在内的多维数据。患者按7:3的比例随机分配到训练集和测试集。使用单因素和多因素逻辑回归分析筛选术后AVN的潜在危险因素。采用六种机器学习算法构建预测模型。利用受试者操作特征曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）、准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、精确率、召回率和F1分数等指标评估预测模型的性能。SHapley加性解释（SHAP）为最优模型提供全局和局部解释。
本研究共纳入207例距骨骨折患者，其中45例（21.74%）发生AVN，162例（78.26%）未发生。单因素和多因素逻辑回归确定了六个独立危险因素，包括体重指数（BMI）、骨折分类、同侧足踝部合并骨折、吸烟、骨折复位质量和骨折类型。性能评估表明，极端梯度提升（XGBoost模型）在训练集和测试集中均获得了较高的AUC值，具有较高的特异性和敏感性。进行SHAP分析以直观地分析模型中特征的相对重要性，并说明每个特征对个体患者结局的影响。
本研究成功开发并验证了一种可解释的机器学习模型，该模型纳入了关键的临床和手术变量，用于预测距骨骨折后的AVN。该预测模型识别出高危患者和关键的可改变因素，有助于制定个性化预防策略以减轻这种严重并发症。
